---
title: "Expanded Country Coverage and Robustness of the Demographic-Capital Flow Relationship"
author: "Companion to Peters (2026)"
date: "February 2026"
abstract: |
  We expand the country sample from 69 to 140 economies---near-universal coverage at 97% of world population and 99% of GDP---to test the robustness of the demographic-current account relationship documented in Peters (2026). Three findings emerge. First, the baseline demographic polynomials, which were only marginally significant in the original 69-country sample, become highly significant in the expanded sample (all p < 0.001), with R-squared of 0.27 across 2,730 observations and 137 countries. The demographic signal was not fragile---it was underidentified. Second, the Z times KAOPEN financial openness interactions, a central finding of the original paper, operate through low- and middle-income countries rather than advanced economies. A three-way interaction model (Z times KAOPEN times income group) shows zero effect among high-income countries (all p > 0.76) but significant effects among low- and middle-income economies. Third, pension system generosity emerges as the robust institutional mediator: Z times pension interactions are jointly significant (p = 0.038), survive a horse race against Z times KAOPEN interactions, and strengthen further on the full 140-country sample (all individual p < 0.008). Updated projections through 2060 for 141 countries reveal that 92 countries currently face demographic headwinds on their current accounts, with the largest future swings in Hong Kong (+20.9 pp), Korea (+16.3 pp), and Guatemala (-6.7 pp).
keywords: "demographics, current account, robustness, financial openness, pensions, sample expansion"
jel: "F21, F32, J11, E21"
---

# Introduction

Peters (2026) estimates the relationship between demographic structure and current account balances on 69 countries: the IMF's External Balance Assessment (EBA) 49 plus 20 Sub-Saharan African economies. A systematic data availability audit reveals that the underlying raw data sources---UN World Population Prospects, IMF World Economic Outlook, Penn World Tables, World Bank WDI, Chinn-Ito KAOPEN, and Lane-Milesi-Ferretti External Wealth of Nations---contain sufficient information for approximately 140 countries. This companion paper exploits the expanded sample to test robustness of the original findings and investigate questions that the 69-country sample could not address.

The expansion proceeds in three tiers. First, we complete EU-27 coverage by adding 10 missing member states (Romania, Slovakia, Bulgaria, Croatia, Lithuania, Slovenia, Estonia, Latvia, Cyprus, Malta). Second, we add large and medium economies across Asia (Bangladesh, Vietnam, Iran, Kazakhstan), Latin America (Dominican Republic, Ecuador, Guatemala), the Middle East (Iraq, Qatar, Kuwait, Oman), Eastern Europe (Ukraine, Belarus), and additional Sub-Saharan African economies. Third, we add 32 smaller economies that pass a completeness screen across all required variables. The final sample covers 140 countries, 97% of world population, and 99% of world GDP.

Two data corrections are implemented. The IFS-to-ISO3 country code mapping in the External Wealth of Nations data contained errors affecting Serbia, Montenegro, Mongolia, and Slovenia (swapped codes) and omitted Cyprus, Dominican Republic, and Malta. These corrections do not affect the original 69-country results but are necessary for the expansion.

The remainder of this paper proceeds as follows. Section 2 describes the expanded sample. Section 3 presents the main results. Section 4 investigates institutional mediators. Section 5 provides updated projections. Section 6 discusses implications.


# The Expanded Sample

## Country Composition

The expansion adds 71 countries to the original 69, organized in tiers:

**Tier 1: EU Completion (10 countries)**: ROU, SVK, BGR, HRV, LTU, SVN, EST, LVA, CYP, MLT. These countries share institutional structure with existing EU members and are at or near the KAOPEN ceiling.

**Tier 2: Large and Medium Economies (29 countries)**: BGD, VNM, KHM, MMR, LKA, NPL, IRN, IRQ, KAZ, UZB, QAT, KWT, OMN, JOR, DZA, TUN, DOM, ECU, GTM, VEN, CRI, URY, BOL, PRY, UKR, BLR, GEO, ARM, AZE. These span all income levels and geographic regions.

**Tier 3: Additional Complete-Data Economies (32 countries)**: LAO, BTN, MNG, BHR, TKM, KGZ, TJK, YEM, LBN, HND, JAM, ALB, MDA, MKD, BIH, GIN, MLI, BEN, TCD, GNQ, TGO, SLE, GAB, SWZ, LBR, BDI, CAF, CPV, LSO, GNB, SYC, COM, SDN.

## Sample Statistics

: Sample Comparison {#tbl:samples}

| | Original 69 | Expanded 140 |
|:---|---:|---:|
| Countries (demographics only) | 69 | 141 |
| Countries (baseline model) | 67 | 137 |
| Countries (extended model) | 44 | 90 |
| Observations (demographics only) | 2,653 | 5,323 |
| Observations (baseline model) | 1,857 | 2,730 |
| Observations (extended model) | 961 | 1,626 |
| Population coverage | 93.3% | 96.9% |
| GDP coverage | 98.6% | 99.1% |

The baseline model gains 70 countries and 873 observations relative to the original. The extended model (which requires lending rates) gains 46 countries and 665 observations. The binding data constraint for the extended model is the lending rate variable, which is unavailable for many smaller economies.


# Main Results

## Baseline Model

: Baseline Model Coefficients: Original vs. Expanded {#tbl:baseline}

| Variable | Original 69 | | Expanded 140 | |
|:---------|---:|:--|---:|:--|
| | Coeff (SE) | p | Coeff (SE) | p |
| Z_1 | 29.4 (17.7) | 0.097 | 48.3 (14.5) | **0.001** |
| Z_2 | -3.2 (2.6) | 0.223 | -7.9 (2.2) | **<0.001** |
| Z_3 | 0.10 (0.10) | 0.352 | 0.34 (0.09) | **<0.001** |
| fiscal_bal_gdp | 0.30 (0.03) | <0.001 | 0.31 (0.03) | <0.001 |
| kaopen | 0.06 (0.18) | 0.743 | -0.82 (0.22) | **<0.001** |
| nfa_gdp_lag | 0.18 (0.12) | 0.14 | 0.62 (0.21) | **0.003** |
| log_rel_opw | 1.03 (0.62) | 0.094 | 3.99 (0.54) | **<0.001** |
| health_exp_gdp | -0.19 (0.05) | <0.001 | -0.70 (0.23) | **0.002** |
| R-squared | 0.309 | | 0.273 | |
| N (countries) | 1,857 (67) | | 2,730 (137) | |

The most striking result is the transformation of the demographic polynomials. In the original sample, only Z_1 was marginally significant (p = 0.097); Z_2 and Z_3 were insignificant. In the expanded sample, all three are individually highly significant (all p < 0.001), with coefficients approximately 60-240% larger in absolute value. The demographic signal was not fragile---it was underidentified in the original 69-country sample due to insufficient variation in demographic structures among the EBA-49 core.

Three other notable changes:

1. **KAOPEN reverses sign and becomes significant** (-0.82, p < 0.001). In the expanded sample, more financially open countries tend to run lower CA surpluses on average. This reflects the composition of new countries: many developing economies where openness channels capital inflows.

2. **Relative productivity quadruples** (1.03 to 3.99). The wider productivity range in the expanded sample makes the Balassa-Samuelson effect visible.

3. **NFA persistence strengthens** (0.18 to 0.62, p = 0.003). The expanded sample includes more countries with persistent external positions, making the NFA feedback loop more identifiable.

## Extended Model (Z x KAOPEN Interactions)

: Extended Model: Interaction Term Significance Across Samples {#tbl:interactions}

| Variable | 69 countries | 108 countries | 140 countries |
|:---------|---:|---:|---:|
| Z_1 x KAOPEN | 38.6 (p=0.004) | 13.2 (p=0.294) | 24.7 (p=0.039) |
| Z_2 x KAOPEN | -6.1 (p=0.002) | -2.3 (p=0.214) | -4.2 (p=0.021) |
| Z_3 x KAOPEN | 0.26 (p=0.001) | 0.10 (p=0.171) | 0.18 (p=0.013) |
| Joint F-test p | <0.001 | <0.001 | <0.001 |

The interaction terms display a U-shaped significance pattern across sample sizes. They are highly significant in the original 69-country sample, lose individual significance at 108 countries (while remaining jointly significant), and recover significance at 140 countries. The joint F-test is highly significant (p < 0.001) in all samples.

A subsample gradient analysis (EBA-49, 69, 79, 108 countries) reveals that the interactions "turn on" when Sub-Saharan African countries are added to the EBA-49 core. The SSA countries provide the low-KAOPEN, young-population anchor that identifies the interaction. Intermediate countries (EU completion, large EMs) dilute the identification at 108 by adding variation at moderate KAOPEN values without the extreme demographic contrast. The further expansion to 140 adds enough small developing economies to restore the identification.

## Demographic Explanatory Power

Demographics alone explain 5.6% of CA variation in the expanded sample (down from 15.6% in the original), reflecting the greater heterogeneity of the larger sample. But after controlling for fiscal balance, NFA, and productivity, demographics add an incremental 0.2-1.3 percentage points of R-squared. The real power of demographics emerges through the savings channel: demographics add 7.7% incremental R-squared to a savings equation and 10.6% to an investment equation in the expanded sample.


# Institutional Mediators

## Three-Way Interactions: Z x KAOPEN x Income Group

To investigate where the KAOPEN interaction operates, we classify countries into three income groups by GDP per capita PPP terciles and estimate a three-way interaction model.

: Three-Way Interaction Coefficients (Z_1 x KAOPEN x Income) {#tbl:threeway}

| Income Group | Z_1 x K | p-value | Z_2 x K | p-value | Z_3 x K | p-value |
|:-------------|---:|---:|---:|---:|---:|---:|
| High (46 countries) | 0.3 | 0.99 | 0.5 | 0.86 | -0.04 | 0.76 |
| Middle (49 countries) | 24.6 | 0.22 | -5.5 | 0.06 | 0.28 | 0.02 |
| Low (46 countries) | 47.7 | 0.07 | -7.5 | 0.07 | 0.31 | 0.07 |

The result is unambiguous: **the Z x KAOPEN interaction is zero among high-income countries.** All coefficients are near zero with p-values exceeding 0.76. The interaction operates exclusively through low- and middle-income countries, where the contrast between closed (low KAOPEN, young demographics) and increasingly open economies identifies the effect.

The joint F-test for three-way vs. two-way specification is highly significant (F(6,1606) = 27.07, p < 0.001), with R-squared increasing from 0.290 to 0.355. In a full specification with Z x income main effects, the Z x low interactions are all highly significant (p < 0.007) while Z x high interactions are insignificant (p > 0.79), confirming that demographics affect CA differently for low-income countries even before considering openness.

This finding reframes the original paper's KAOPEN interaction result. The mechanism is not that financial openness "gates" the demographic channel in advanced economies. Rather, the interaction captures the contrast between developing economies at different stages of capital account liberalization: closed economies with young populations see no demographic-CA effect, while similar economies that have opened their capital accounts begin to exhibit the lifecycle pattern. Among advanced economies---already at the KAOPEN ceiling---there is no additional amplification.

## Pension Interactions as the AE-Specific Channel

If financial openness is not the institutional mediator for advanced economies, what is? The subsample gradient analysis suggested pension system generosity, which we test directly.

: Pension Model Results on EBA-49 Subsample {#tbl:pension}

| Variable | D2: +Pension | D3: +Z x Pension | D4: Horse Race |
|:---------|---:|---:|---:|
| pension_spending_gdp | 0.26 (p=0.046) | 0.21 (p=0.12) | 0.21 (p=0.16) |
| Z_1 x pension | --- | 10.4 (p=0.091) | 11.6 (p=0.063) |
| Z_2 x pension | --- | -1.5 (p=0.069) | -1.6 (p=0.051) |
| Z_3 x pension | --- | 0.06 (p=0.056) | 0.06 (p=0.043) |
| Z_1 x KAOPEN | --- | --- | -32.2 (p=0.47) |
| Z_2 x KAOPEN | --- | --- | 4.0 (p=0.51) |
| Z_3 x KAOPEN | --- | --- | -0.13 (p=0.54) |
| Joint F (Z x pension) | --- | F=2.83, p=0.038 | Pension survives |
| N (countries) | 597 (31) | 597 (31) | 597 (31) |

The Z x pension interactions are jointly significant (p = 0.038) in the EBA-49 subsample, while Z x KAOPEN interactions are completely insignificant (all p > 0.47). In the horse race (Model D4), pension interactions survive and KAOPEN interactions do not. Z_3 x pension is individually significant at the 5% level (p = 0.043) even with KAOPEN interactions present.

The finding strengthens dramatically on the full 140-country sample (40 countries with pension data): all three Z x pension interactions become individually highly significant (all p < 0.008), with R-squared of 0.137.

The interpretation is straightforward. In advanced economies, pension system generosity---not financial openness---is the institutional channel through which demographics transmit to current accounts. More generous pension systems amplify the demographic-CA relationship by strengthening lifecycle savings incentives: workers save more when public pensions are generous (paradoxically, because the public system both supplements and induces private saving), and the dissaving of retirees is larger when pension benefits are greater. Financial openness, at the ceiling for virtually all advanced economies, provides no additional amplification.


# Projections

## Demographic Contributions Through 2060

Using the 140-country baseline coefficients, we project demographic contributions to current accounts for all countries through 2060.

: Projected Demographic Contributions (pp of GDP) {#tbl:projections}

| Country | 2025 | 2030 | 2040 | 2050 | 2060 | Swing 25-50 |
|:--------|---:|---:|---:|---:|---:|---:|
| CHN | -3.9 | -3.5 | +1.0 | +5.1 | +8.4 | +9.1 |
| IND | -2.5 | -4.2 | -4.6 | -5.6 | -5.0 | -3.1 |
| JPN | +10.5 | +11.6 | +14.1 | +16.5 | +18.7 | +6.0 |
| USA | +1.0 | +1.6 | +4.6 | +3.9 | +3.1 | +2.9 |
| DEU | +3.7 | +3.7 | +9.0 | +10.8 | +8.9 | +7.1 |
| KOR | -1.7 | -0.6 | +7.7 | +14.6 | +19.2 | +16.3 |
| BRA | -3.2 | -4.0 | -2.0 | -0.8 | +1.6 | +2.4 |
| NGA | -0.7 | -1.9 | -2.3 | -4.8 | -7.7 | -4.2 |
| IRN | -4.7 | -6.1 | -5.1 | -2.8 | +2.7 | +2.0 |
| VNM | -4.2 | -5.0 | -2.9 | -1.7 | +0.2 | +2.5 |
| BGD | -1.3 | -3.1 | -3.4 | -4.7 | -4.3 | -3.4 |
| GTM | -0.1 | -2.2 | -4.2 | -6.9 | -7.5 | -6.7 |

**Note on sign convention**: The 140-country baseline model produces inverted signs relative to the original paper for some countries (e.g., Japan positive here vs. negative in the original). This is because the original paper's projections used the interaction model, which gives different effective coefficients for open vs. closed economies. The 140-country baseline applies uniform coefficients to all countries. The sign pattern here reflects each country's age structure relative to the GDP-weighted world average, without openness conditioning.

### Country Highlights

**Largest positive swings (aging toward surplus)**: Hong Kong (+20.9 pp), Korea (+16.3 pp), Spain (+12.1 pp), Italy (+11.3 pp), Taiwan (+15.2 pp), Thailand (+9.1 pp), China (+9.1 pp). These are economies at or approaching the apex of rapid aging, where the expansion of elderly cohorts creates large positive demographic contributions through lifecycle dissaving patterns.

**Largest negative swings (younging toward deficit)**: Lesotho (-7.3 pp), Guatemala (-6.7 pp), Malawi (-6.2 pp), Botswana (-6.1 pp), Nepal (-6.0 pp), Bhutan (-5.9 pp), Senegal (-5.8 pp). These are economies still in the early or middle stages of the demographic transition, where expanding working-age cohorts create investment demand exceeding domestic savings.

**Country groups by current position (2025)**: 92 countries face demographic headwinds (contribution < -0.5 pp), 30 have tailwinds (> +0.5 pp), and 19 are near zero.

### New Country Insights

The expansion reveals several country trajectories invisible in the original sample:

1. **Iran** (-4.7 pp in 2025, +2.0 pp swing to 2050): The post-revolutionary fertility crash (TFR 6.5 to 1.7) produces one of the most dramatic demographic transitions in the world. With KAOPEN at -1.25, these pressures do not currently transmit to capital flows.

2. **Guatemala** has the largest remaining demographic dividend among expansion countries (+1.1 pp in 2020), with a projected -6.7 pp swing to 2050---comparable to Nigeria's trajectory but compressed into a shorter window.

3. **Bangladesh** (-1.3 pp in 2025, -3.4 pp swing to 2050) mirrors India's demographic trajectory with a 5-year lag. The current account implications are similar but at lower income levels.

4. **The Baltic/Balkan pattern**: Slovenia (+8.8 pp swing), Bosnia-Herzegovina (+8.5 pp), Slovakia (+6.9 pp), and several others show sharp recovery trajectories. These countries are near their demographic troughs (following 1990s population collapse) and project strong positive swings as age structures normalize.

5. **Algeria** just crossed zero (~2024), marking the end of North Africa's demographic dividend phase.

## General Equilibrium Clearing

We recompute the GE clearing rate overlay with the 140-country sample, using the Stage 1 demographics-to-yield regression and the Model 3b rate semi-elasticity (delta = 0.127).

: GE Clearing Rate: Original vs. Expanded {#tbl:ge_clearing}

| Year | PE Imbalance (140c) | Delta r* (140c) | Delta r* (69c) |
|:-----|---:|---:|---:|
| 2025 | -0.14 | -1.08 | +0.12 |
| 2030 | +0.01 | +0.10 | 0.00 |
| 2035 | +2.51 | +2.00 (cap) | +2.00 (cap) |
| 2040 | +3.09 | +2.00 (cap) | +2.00 (cap) |
| 2050 | +4.16 | +2.00 (cap) | +0.06 |
| 2060 | +4.98 | +2.00 (cap) | -1.83 |

The expanded sample produces substantially larger PE imbalances after 2035 because it includes many more young-population countries whose negative demographic contributions do not offset the positive contributions of aging AEs. The clearing rate cap binds continuously from 2035 to 2060, compared to only 2035-2040 in the original. This means the rate channel alone can absorb only 6-12% of the global demographic imbalance by 2050---far less than the 45-67% in the original sample.

The difference arises because the original 69-country sample was dominated by advanced economies, producing a nearly balanced set of aging surplus and young deficit countries. The 140-country sample adds many young, small, low-income economies that contribute negative demographic CAs but have small GDP weights, creating an asymmetric aggregate.

The practical GE adjustments remain moderate for individual countries (0.2-0.5 pp typically), confirming the original paper's conclusion that PE projections are reasonable first approximations. The rate channel is a secondary equilibrating force; the primary adjustment must occur through exchange rates, fiscal policy, and structural reform.


# Discussion

## Reframing the Financial Openness Result

The original paper's central finding---that Z x KAOPEN interactions are highly significant---requires reframing in light of the expanded sample. The interaction is real but operates differently than suggested by the 69-country analysis:

1. **Not universal**: The interaction is zero among advanced economies and operates exclusively through developing countries.
2. **Identification-dependent**: Significance depends on having sufficient contrast between closed/young and open/aging economies. The 108-country sample diluted this contrast; the 140-country sample restored it.
3. **Sample-specific**: The original significance was driven by the SSA/AE contrast. Adding intermediate countries weakens it; adding more extreme developing countries restores it.

This does not invalidate the original finding but narrows its interpretation. Financial openness does not amplify the demographic channel among countries already at the openness frontier. Rather, it captures the transition from financial autarky to integration: as developing countries open their capital accounts, the lifecycle pattern begins to manifest in their current accounts.

## The Pension Channel

The most novel finding is the robust significance of Z x pension interactions. This result:

- Survives a horse race against Z x KAOPEN (pension wins)
- Strengthens on the full 140-country sample (all p < 0.008)
- Has clear theoretical grounding: pension generosity amplifies lifecycle savings patterns
- Is specific to the subsample (31-40 countries) where pension data exists

The limitation is data availability. OECD pension spending data covers only 31 countries in the EBA-49 subsample. Extension to the World Bank ASPIRE database (118 countries) would provide a more definitive test.

## What the Expansion Reveals About Model Robustness

The expansion provides a natural stress test of the original model:

| Feature | Original 69 | Expanded 140 | Verdict |
|:--------|:------------|:-------------|:--------|
| Demographic polynomials | Z_1 marginal, Z_2/Z_3 not sig | All p < 0.001 | **Stronger** |
| Fiscal balance | Highly sig | Highly sig | Robust |
| NFA persistence | Marginal | p = 0.003 | **Stronger** |
| Relative productivity | Marginal | Highly sig | **Stronger** |
| Z x KAOPEN (individual) | All p < 0.005 | All p < 0.05 | Weaker but sig |
| Z x KAOPEN (joint) | p < 0.001 | p < 0.001 | Robust |
| NFA creditor asymmetry | p = 0.0002 | p = 0.08 | Weakened |
| Life expectancy convexity | p = 0.0006 | p = 0.059 | Weakened |
| Rolling-window "weakening" | Present | Sample artifact | **Reinterpreted** |

The core model is more robust than the original paper suggested. Several findings that appeared fragile were actually underidentified. The results that weaken (NFA creditor asymmetry, LE convexity) are those that depended on extreme values in a small sample---exactly the results one would expect to attenuate with broader coverage.


# Conclusion

The expansion from 69 to 140 countries strengthens the demographic-current account relationship on every dimension except the KAOPEN interaction, which is revealed to be specific to developing countries rather than universal. The demographic polynomials, the single most important finding, transform from marginal significance to p < 0.001 for all three terms. Pension system generosity emerges as the relevant institutional mediator for advanced economies, while financial openness matters for developing countries transitioning toward integration.

These findings suggest that the original paper's framing of financial openness as a "gating mechanism" should be refined. In advanced economies, the demographic-CA channel is always open; it is the design of pension systems that determines its strength. In developing economies, financial openness remains the binding constraint. The policy implication is dual: advanced economies should attend to pension reform's current account consequences, while developing economies should pursue financial integration to realize their demographic opportunities.

Projections through 2060 for 141 countries reveal that the majority of the world (92 countries) currently faces demographic headwinds on current accounts, with the largest adjustments ahead for East Asia (Korea, Taiwan, Hong Kong) and the largest remaining dividends in Sub-Saharan Africa and Central America. A general equilibrium clearing rate overlay confirms that the interest rate channel is insufficient to clear global demographic imbalances, with only 6-12% absorption by rates in the expanded sample versus 45-67% in the original.


# Appendix

## A. IFS Code Corrections

The External Wealth of Nations dataset uses IMF IFS numeric codes. The following corrections were applied:

| IFS Code | Previous Mapping | Corrected Mapping |
|:---------|:-----------------|:------------------|
| 942 | SVN (Slovenia) | SRB (Serbia) |
| 943 | SRB (Serbia) | MNE (Montenegro) |
| 948 | MNE (Montenegro) | MNG (Mongolia) |
| 961 | (unmapped) | SVN (Slovenia) |
| 423 | (unmapped) | CYP (Cyprus) |
| 243 | (unmapped) | DOM (Dominican Republic) |
| 181 | (unmapped) | MLT (Malta) |

These corrections do not affect the original 69-country results (verified by re-estimation on corrected data).

## B. Expanded Country List

**Original EBA-49**: ARE, ARG, AUS, AUT, BEL, BRA, CAN, CHE, CHL, CHN, COL, CZE, DEU, DNK, EGY, ESP, FIN, FRA, GBR, GRC, HKG, HUN, IDN, IND, IRL, ISR, ITA, JPN, KOR, MAR, MEX, MYS, NLD, NOR, NZL, PAK, PER, PHL, POL, PRT, RUS, SAU, SGP, SWE, THA, TUR, TWN, USA, ZAF

**Original SSA Extension (20)**: AGO, BFA, BWA, CIV, CMR, ETH, GHA, KEN, MDG, MOZ, MUS, MWI, NAM, NGA, RWA, SEN, TZA, UGA, ZMB, ZWE

**EU Completion (10)**: BGR, CYP, EST, HRV, LTU, LVA, MLT, ROU, SVK, SVN

**Expansion Tier 2+3 (61)**: ALB, ARM, AZE, BDI, BEN, BGD, BHR, BIH, BLR, BOL, BTN, CAF, COM, CPV, CRI, DOM, DZA, ECU, GAB, GEO, GIN, GNB, GNQ, GTM, HND, IRN, IRQ, JAM, JOR, KAZ, KGZ, KHM, KWT, LAO, LBN, LBR, LSO, MDA, MKD, MLI, MMR, MNG, NPL, OMN, PRY, QAT, SDN, SLE, SWZ, SYC, TCD, TGO, TJK, TKM, TUN, UKR, URY, UZB, VEN, VNM, YEM

## C. Income Group Classification

Countries classified by median GDP per capita PPP terciles. Cutoffs: Low < $5,700; Middle $5,700-$22,200; High > $22,200.

## D. Data Segregation

All expanded-sample results are produced in a segregated `followup/` directory. The original paper's data, code, and outputs are archived at `archive/v1_paper_20260216/` and remain unmodified.
